/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    RandomSplitResultProducer.java
 *    Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.experiment;

import java.io.File;
import java.util.Calendar;
import java.util.Collections;
import java.util.Enumeration;
import java.util.Random;
import java.util.TimeZone;
import java.util.Vector;

import weka.core.AdditionalMeasureProducer;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Utils;

/**
 * <!-- globalinfo-start --> Generates a single train/test split and calls the
 * appropriate SplitEvaluator to generate some results.
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 * -P &lt;percent&gt;
 *  The percentage of instances to use for training.
 *  (default 66)
 * </pre>
 * 
 * <pre>
 * -D
 * Save raw split evaluator output.
 * </pre>
 * 
 * <pre>
 * -O &lt;file/directory name/path&gt;
 *  The filename where raw output will be stored.
 *  If a directory name is specified then then individual
 *  outputs will be gzipped, otherwise all output will be
 *  zipped to the named file. Use in conjuction with -D. (default splitEvalutorOut.zip)
 * </pre>
 * 
 * <pre>
 * -W &lt;class name&gt;
 *  The full class name of a SplitEvaluator.
 *  eg: weka.experiment.ClassifierSplitEvaluator
 * </pre>
 * 
 * <pre>
 * -R
 *  Set when data is not to be randomized and the data sets' size.
 *  Is not to be determined via probabilistic rounding.
 * </pre>
 * 
 * <pre>
 * Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
 * </pre>
 * 
 * <pre>
 * -W &lt;class name&gt;
 *  The full class name of the classifier.
 *  eg: weka.classifiers.bayes.NaiveBayes
 * </pre>
 * 
 * <pre>
 * -C &lt;index&gt;
 *  The index of the class for which IR statistics
 *  are to be output. (default 1)
 * </pre>
 * 
 * <pre>
 * -I &lt;index&gt;
 *  The index of an attribute to output in the
 *  results. This attribute should identify an
 *  instance in order to know which instances are
 *  in the test set of a cross validation. if 0
 *  no output (default 0).
 * </pre>
 * 
 * <pre>
 * -P
 *  Add target and prediction columns to the result
 *  for each fold.
 * </pre>
 * 
 * <pre>
 * Options specific to classifier weka.classifiers.rules.ZeroR:
 * </pre>
 * 
 * <pre>
 * -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
 * </pre>
 * 
 * <!-- options-end -->
 * 
 * All options after -- will be passed to the split evaluator.
 * 
 * @author Len Trigg (trigg@cs.waikato.ac.nz)
 * @version $Revision$
 */
public class RandomSplitResultProducer implements ResultProducer, OptionHandler, AdditionalMeasureProducer {

    /** for serialization */
    static final long serialVersionUID = 1403798165056795073L;

    /** The dataset of interest */
    protected Instances m_Instances;

    /** The ResultListener to send results to */
    protected ResultListener m_ResultListener = new CSVResultListener();

    /** The percentage of instances to use for training */
    protected double m_TrainPercent = 66;

    /** Whether dataset is to be randomized */
    protected boolean m_randomize = true;

    /** The SplitEvaluator used to generate results */
    protected SplitEvaluator m_SplitEvaluator = new ClassifierSplitEvaluator();

    /** The names of any additional measures to look for in SplitEvaluators */
    protected String[] m_AdditionalMeasures = null;

    /** Save raw output of split evaluators --- for debugging purposes */
    protected boolean m_debugOutput = false;

    /** The output zipper to use for saving raw splitEvaluator output */
    protected OutputZipper m_ZipDest = null;

    /** The destination output file/directory for raw output */
    protected File m_OutputFile = new File(new File(System.getProperty("user.dir")), "splitEvalutorOut.zip");

    /** The name of the key field containing the dataset name */
    public static String DATASET_FIELD_NAME = "Dataset";

    /** The name of the key field containing the run number */
    public static String RUN_FIELD_NAME = "Run";

    /** The name of the result field containing the timestamp */
    public static String TIMESTAMP_FIELD_NAME = "Date_time";

    /**
     * Returns a string describing this result producer
     * 
     * @return a description of the result producer suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String globalInfo() {
        return "Generates a single train/test split and calls the appropriate " + "SplitEvaluator to generate some results.";
    }

    /**
     * Sets the dataset that results will be obtained for.
     * 
     * @param instances a value of type 'Instances'.
     */
    @Override
    public void setInstances(Instances instances) {

        m_Instances = instances;
    }

    /**
     * Set a list of method names for additional measures to look for in
     * SplitEvaluators. This could contain many measures (of which only a subset may
     * be produceable by the current SplitEvaluator) if an experiment is the type
     * that iterates over a set of properties.
     * 
     * @param additionalMeasures an array of measure names, null if none
     */
    @Override
    public void setAdditionalMeasures(String[] additionalMeasures) {
        m_AdditionalMeasures = additionalMeasures;

        if (m_SplitEvaluator != null) {
            System.err.println("RandomSplitResultProducer: setting additional " + "measures for " + "split evaluator");
            m_SplitEvaluator.setAdditionalMeasures(m_AdditionalMeasures);
        }
    }

    /**
     * Returns an enumeration of any additional measure names that might be in the
     * SplitEvaluator
     * 
     * @return an enumeration of the measure names
     */
    @Override
    public Enumeration<String> enumerateMeasures() {
        Vector<String> newVector = new Vector<String>();
        if (m_SplitEvaluator instanceof AdditionalMeasureProducer) {
            Enumeration<String> en = ((AdditionalMeasureProducer) m_SplitEvaluator).enumerateMeasures();
            while (en.hasMoreElements()) {
                String mname = en.nextElement();
                newVector.add(mname);
            }
        }
        return newVector.elements();
    }

    /**
     * Returns the value of the named measure
     * 
     * @param additionalMeasureName the name of the measure to query for its value
     * @return the value of the named measure
     * @throws IllegalArgumentException if the named measure is not supported
     */
    @Override
    public double getMeasure(String additionalMeasureName) {
        if (m_SplitEvaluator instanceof AdditionalMeasureProducer) {
            return ((AdditionalMeasureProducer) m_SplitEvaluator).getMeasure(additionalMeasureName);
        } else {
            throw new IllegalArgumentException("RandomSplitResultProducer: " + "Can't return value for : " + additionalMeasureName + ". " + m_SplitEvaluator.getClass().getName() + " " + "is not an AdditionalMeasureProducer");
        }
    }

    /**
     * Sets the object to send results of each run to.
     * 
     * @param listener a value of type 'ResultListener'
     */
    @Override
    public void setResultListener(ResultListener listener) {

        m_ResultListener = listener;
    }

    /**
     * Gets a Double representing the current date and time. eg: 1:46pm on 20/5/1999
     * -> 19990520.1346
     * 
     * @return a value of type Double
     */
    public static Double getTimestamp() {

        Calendar now = Calendar.getInstance(TimeZone.getTimeZone("UTC"));
        double timestamp = now.get(Calendar.YEAR) * 10000 + (now.get(Calendar.MONTH) + 1) * 100 + now.get(Calendar.DAY_OF_MONTH) + now.get(Calendar.HOUR_OF_DAY) / 100.0 + now.get(Calendar.MINUTE) / 10000.0;
        return new Double(timestamp);
    }

    /**
     * Prepare to generate results.
     * 
     * @throws Exception if an error occurs during preprocessing.
     */
    @Override
    public void preProcess() throws Exception {

        if (m_SplitEvaluator == null) {
            throw new Exception("No SplitEvalutor set");
        }
        if (m_ResultListener == null) {
            throw new Exception("No ResultListener set");
        }
        m_ResultListener.preProcess(this);
    }

    /**
     * Perform any postprocessing. When this method is called, it indicates that no
     * more requests to generate results for the current experiment will be sent.
     * 
     * @throws Exception if an error occurs
     */
    @Override
    public void postProcess() throws Exception {

        m_ResultListener.postProcess(this);
        if (m_debugOutput) {
            if (m_ZipDest != null) {
                m_ZipDest.finished();
                m_ZipDest = null;
            }
        }
    }

    /**
     * Gets the keys for a specified run number. Different run numbers correspond to
     * different randomizations of the data. Keys produced should be sent to the
     * current ResultListener
     * 
     * @param run the run number to get keys for.
     * @throws Exception if a problem occurs while getting the keys
     */
    @Override
    public void doRunKeys(int run) throws Exception {
        if (m_Instances == null) {
            throw new Exception("No Instances set");
        }
        // Add in some fields to the key like run number, dataset name
        Object[] seKey = m_SplitEvaluator.getKey();
        Object[] key = new Object[seKey.length + 2];
        key[0] = Utils.backQuoteChars(m_Instances.relationName());
        key[1] = "" + run;
        System.arraycopy(seKey, 0, key, 2, seKey.length);
        if (m_ResultListener.isResultRequired(this, key)) {
            try {
                m_ResultListener.acceptResult(this, key, null);
            } catch (Exception ex) {
                // Save the train and test datasets for debugging purposes?
                throw ex;
            }
        }
    }

    /**
     * Gets the results for a specified run number. Different run numbers correspond
     * to different randomizations of the data. Results produced should be sent to
     * the current ResultListener
     * 
     * @param run the run number to get results for.
     * @throws Exception if a problem occurs while getting the results
     */
    @Override
    public void doRun(int run) throws Exception {

        if (getRawOutput()) {
            if (m_ZipDest == null) {
                m_ZipDest = new OutputZipper(m_OutputFile);
            }
        }

        if (m_Instances == null) {
            throw new Exception("No Instances set");
        }
        // Add in some fields to the key like run number, dataset name
        Object[] seKey = m_SplitEvaluator.getKey();
        Object[] key = new Object[seKey.length + 2];
        key[0] = Utils.backQuoteChars(m_Instances.relationName());
        key[1] = "" + run;
        System.arraycopy(seKey, 0, key, 2, seKey.length);
        if (m_ResultListener.isResultRequired(this, key)) {

            // Randomize on a copy of the original dataset
            Instances runInstances = new Instances(m_Instances);

            Instances train;
            Instances test;

            if (!m_randomize) {

                // Don't do any randomization
                int trainSize = Utils.round(runInstances.numInstances() * m_TrainPercent / 100);
                int testSize = runInstances.numInstances() - trainSize;
                train = new Instances(runInstances, 0, trainSize);
                test = new Instances(runInstances, trainSize, testSize);
            } else {
                Random rand = new Random(run);
                runInstances.randomize(rand);

                // Nominal class
                if (runInstances.classAttribute().isNominal()) {

                    // create the subset for each classs
                    int numClasses = runInstances.numClasses();
                    Instances[] subsets = new Instances[numClasses + 1];
                    for (int i = 0; i < numClasses + 1; i++) {
                        subsets[i] = new Instances(runInstances, 10);
                    }

                    // divide instances into subsets
                    Enumeration<Instance> e = runInstances.enumerateInstances();
                    while (e.hasMoreElements()) {
                        Instance inst = e.nextElement();
                        if (inst.classIsMissing()) {
                            subsets[numClasses].add(inst);
                        } else {
                            subsets[(int) inst.classValue()].add(inst);
                        }
                    }

                    // Compactify them
                    for (int i = 0; i < numClasses + 1; i++) {
                        subsets[i].compactify();
                    }

                    // merge into train and test sets
                    train = new Instances(runInstances, runInstances.numInstances());
                    test = new Instances(runInstances, runInstances.numInstances());
                    for (int i = 0; i < numClasses + 1; i++) {
                        int trainSize = Utils.probRound(subsets[i].numInstances() * m_TrainPercent / 100, rand);
                        for (int j = 0; j < trainSize; j++) {
                            train.add(subsets[i].instance(j));
                        }
                        for (int j = trainSize; j < subsets[i].numInstances(); j++) {
                            test.add(subsets[i].instance(j));
                        }
                        // free memory
                        subsets[i] = null;
                    }
                    train.compactify();
                    test.compactify();

                    // randomize the final sets
                    train.randomize(rand);
                    test.randomize(rand);
                } else {

                    // Numeric target
                    int trainSize = Utils.probRound(runInstances.numInstances() * m_TrainPercent / 100, rand);
                    int testSize = runInstances.numInstances() - trainSize;
                    train = new Instances(runInstances, 0, trainSize);
                    test = new Instances(runInstances, trainSize, testSize);
                }
            }
            try {
                Object[] seResults = m_SplitEvaluator.getResult(train, test);
                Object[] results = new Object[seResults.length + 1];
                results[0] = getTimestamp();
                System.arraycopy(seResults, 0, results, 1, seResults.length);
                if (m_debugOutput) {
                    String resultName = ("" + run + "." + Utils.backQuoteChars(runInstances.relationName()) + "." + m_SplitEvaluator.toString()).replace(' ', '_');
                    resultName = Utils.removeSubstring(resultName, "weka.classifiers.");
                    resultName = Utils.removeSubstring(resultName, "weka.filters.");
                    resultName = Utils.removeSubstring(resultName, "weka.attributeSelection.");
                    m_ZipDest.zipit(m_SplitEvaluator.getRawResultOutput(), resultName);
                }
                m_ResultListener.acceptResult(this, key, results);
            } catch (Exception ex) {
                // Save the train and test datasets for debugging purposes?
                throw ex;
            }
        }
    }

    /**
     * Gets the names of each of the columns produced for a single run. This method
     * should really be static.
     * 
     * @return an array containing the name of each column
     */
    @Override
    public String[] getKeyNames() {

        String[] keyNames = m_SplitEvaluator.getKeyNames();
        // Add in the names of our extra key fields
        String[] newKeyNames = new String[keyNames.length + 2];
        newKeyNames[0] = DATASET_FIELD_NAME;
        newKeyNames[1] = RUN_FIELD_NAME;
        System.arraycopy(keyNames, 0, newKeyNames, 2, keyNames.length);
        return newKeyNames;
    }

    /**
     * Gets the data types of each of the columns produced for a single run. This
     * method should really be static.
     * 
     * @return an array containing objects of the type of each column. The objects
     *         should be Strings, or Doubles.
     */
    @Override
    public Object[] getKeyTypes() {

        Object[] keyTypes = m_SplitEvaluator.getKeyTypes();
        // Add in the types of our extra fields
        Object[] newKeyTypes = new String[keyTypes.length + 2];
        newKeyTypes[0] = new String();
        newKeyTypes[1] = new String();
        System.arraycopy(keyTypes, 0, newKeyTypes, 2, keyTypes.length);
        return newKeyTypes;
    }

    /**
     * Gets the names of each of the columns produced for a single run. This method
     * should really be static.
     * 
     * @return an array containing the name of each column
     */
    @Override
    public String[] getResultNames() {

        String[] resultNames = m_SplitEvaluator.getResultNames();
        // Add in the names of our extra Result fields
        String[] newResultNames = new String[resultNames.length + 1];
        newResultNames[0] = TIMESTAMP_FIELD_NAME;
        System.arraycopy(resultNames, 0, newResultNames, 1, resultNames.length);
        return newResultNames;
    }

    /**
     * Gets the data types of each of the columns produced for a single run. This
     * method should really be static.
     * 
     * @return an array containing objects of the type of each column. The objects
     *         should be Strings, or Doubles.
     */
    @Override
    public Object[] getResultTypes() {

        Object[] resultTypes = m_SplitEvaluator.getResultTypes();
        // Add in the types of our extra Result fields
        Object[] newResultTypes = new Object[resultTypes.length + 1];
        newResultTypes[0] = new Double(0);
        System.arraycopy(resultTypes, 0, newResultTypes, 1, resultTypes.length);
        return newResultTypes;
    }

    /**
     * Gets a description of the internal settings of the result producer,
     * sufficient for distinguishing a ResultProducer instance from another with
     * different settings (ignoring those settings set through this interface). For
     * example, a cross-validation ResultProducer may have a setting for the number
     * of folds. For a given state, the results produced should be compatible.
     * Typically if a ResultProducer is an OptionHandler, this string will represent
     * the command line arguments required to set the ResultProducer to that state.
     * 
     * @return the description of the ResultProducer state, or null if no state is
     *         defined
     */
    @Override
    public String getCompatibilityState() {

        String result = "-P " + m_TrainPercent;
        if (!getRandomizeData()) {
            result += " -R";
        }
        if (m_SplitEvaluator == null) {
            result += " <null SplitEvaluator>";
        } else {
            result += " -W " + m_SplitEvaluator.getClass().getName();
        }
        return result + " --";
    }

    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String outputFileTipText() {
        return "Set the destination for saving raw output. If the rawOutput " + "option is selected, then output from the splitEvaluator for " + "individual train-test splits is saved. If the destination is a " + "directory, " + "then each output is saved to an individual gzip file; if the " + "destination is a file, then each output is saved as an entry " + "in a zip file.";
    }

    /**
     * Get the value of OutputFile.
     * 
     * @return Value of OutputFile.
     */
    public File getOutputFile() {

        return m_OutputFile;
    }

    /**
     * Set the value of OutputFile.
     * 
     * @param newOutputFile Value to assign to OutputFile.
     */
    public void setOutputFile(File newOutputFile) {

        m_OutputFile = newOutputFile;
    }

    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String randomizeDataTipText() {
        return "Do not randomize dataset and do not perform probabilistic rounding " + "if false";
    }

    /**
     * Get if dataset is to be randomized
     * 
     * @return true if dataset is to be randomized
     */
    public boolean getRandomizeData() {
        return m_randomize;
    }

    /**
     * Set to true if dataset is to be randomized
     * 
     * @param d true if dataset is to be randomized
     */
    public void setRandomizeData(boolean d) {
        m_randomize = d;
    }

    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String rawOutputTipText() {
        return "Save raw output (useful for debugging). If set, then output is " + "sent to the destination specified by outputFile";
    }

    /**
     * Get if raw split evaluator output is to be saved
     * 
     * @return true if raw split evalutor output is to be saved
     */
    public boolean getRawOutput() {
        return m_debugOutput;
    }

    /**
     * Set to true if raw split evaluator output is to be saved
     * 
     * @param d true if output is to be saved
     */
    public void setRawOutput(boolean d) {
        m_debugOutput = d;
    }

    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String trainPercentTipText() {
        return "Set the percentage of data to use for training.";
    }

    /**
     * Get the value of TrainPercent.
     * 
     * @return Value of TrainPercent.
     */
    public double getTrainPercent() {

        return m_TrainPercent;
    }

    /**
     * Set the value of TrainPercent.
     * 
     * @param newTrainPercent Value to assign to TrainPercent.
     */
    public void setTrainPercent(double newTrainPercent) {

        m_TrainPercent = newTrainPercent;
    }

    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String splitEvaluatorTipText() {
        return "The evaluator to apply to the test data. " + "This may be a classifier, regression scheme etc.";
    }

    /**
     * Get the SplitEvaluator.
     * 
     * @return the SplitEvaluator.
     */
    public SplitEvaluator getSplitEvaluator() {

        return m_SplitEvaluator;
    }

    /**
     * Set the SplitEvaluator.
     * 
     * @param newSplitEvaluator new SplitEvaluator to use.
     */
    public void setSplitEvaluator(SplitEvaluator newSplitEvaluator) {

        m_SplitEvaluator = newSplitEvaluator;
        m_SplitEvaluator.setAdditionalMeasures(m_AdditionalMeasures);
    }

    /**
     * Returns an enumeration describing the available options..
     * 
     * @return an enumeration of all the available options.
     */
    @Override
    public Enumeration<Option> listOptions() {

        Vector<Option> newVector = new Vector<Option>(5);

        newVector.addElement(new Option("\tThe percentage of instances to use for training.\n" + "\t(default 66)", "P", 1, "-P <percent>"));

        newVector.addElement(new Option("Save raw split evaluator output.", "D", 0, "-D"));

        newVector.addElement(new Option("\tThe filename where raw output will be stored.\n" + "\tIf a directory name is specified then then individual\n" + "\toutputs will be gzipped, otherwise all output will be\n" + "\tzipped to the named file. Use in conjuction with -D." + "\t(default splitEvalutorOut.zip)", "O", 1, "-O <file/directory name/path>"));

        newVector.addElement(new Option("\tThe full class name of a SplitEvaluator.\n" + "\teg: weka.experiment.ClassifierSplitEvaluator", "W", 1, "-W <class name>"));

        newVector.addElement(new Option("\tSet when data is not to be randomized and the data sets' size.\n" + "\tIs not to be determined via probabilistic rounding.", "R", 0, "-R"));

        if ((m_SplitEvaluator != null) && (m_SplitEvaluator instanceof OptionHandler)) {
            newVector.addElement(new Option("", "", 0, "\nOptions specific to split evaluator " + m_SplitEvaluator.getClass().getName() + ":"));
            newVector.addAll(Collections.list(((OptionHandler) m_SplitEvaluator).listOptions()));
        }
        return newVector.elements();
    }

    /**
     * Parses a given list of options.
     * <p/>
     * 
     * <!-- options-start --> Valid options are:
     * <p/>
     * 
     * <pre>
     * -P &lt;percent&gt;
     *  The percentage of instances to use for training.
     *  (default 66)
     * </pre>
     * 
     * <pre>
     * -D
     * Save raw split evaluator output.
     * </pre>
     * 
     * <pre>
     * -O &lt;file/directory name/path&gt;
     *  The filename where raw output will be stored.
     *  If a directory name is specified then then individual
     *  outputs will be gzipped, otherwise all output will be
     *  zipped to the named file. Use in conjuction with -D. (default splitEvalutorOut.zip)
     * </pre>
     * 
     * <pre>
     * -W &lt;class name&gt;
     *  The full class name of a SplitEvaluator.
     *  eg: weka.experiment.ClassifierSplitEvaluator
     * </pre>
     * 
     * <pre>
     * -R
     *  Set when data is not to be randomized and the data sets' size.
     *  Is not to be determined via probabilistic rounding.
     * </pre>
     * 
     * <pre>
     * Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
     * </pre>
     * 
     * <pre>
     * -W &lt;class name&gt;
     *  The full class name of the classifier.
     *  eg: weka.classifiers.bayes.NaiveBayes
     * </pre>
     * 
     * <pre>
     * -C &lt;index&gt;
     *  The index of the class for which IR statistics
     *  are to be output. (default 1)
     * </pre>
     * 
     * <pre>
     * -I &lt;index&gt;
     *  The index of an attribute to output in the
     *  results. This attribute should identify an
     *  instance in order to know which instances are
     *  in the test set of a cross validation. if 0
     *  no output (default 0).
     * </pre>
     * 
     * <pre>
     * -P
     *  Add target and prediction columns to the result
     *  for each fold.
     * </pre>
     * 
     * <pre>
     * Options specific to classifier weka.classifiers.rules.ZeroR:
     * </pre>
     * 
     * <pre>
     * -D
     *  If set, classifier is run in debug mode and
     *  may output additional info to the console
     * </pre>
     * 
     * <!-- options-end -->
     * 
     * All options after -- will be passed to the split evaluator.
     * 
     * @param options the list of options as an array of strings
     * @throws Exception if an option is not supported
     */
    @Override
    public void setOptions(String[] options) throws Exception {

        setRawOutput(Utils.getFlag('D', options));
        setRandomizeData(!Utils.getFlag('R', options));

        String fName = Utils.getOption('O', options);
        if (fName.length() != 0) {
            setOutputFile(new File(fName));
        }

        String trainPct = Utils.getOption('P', options);
        if (trainPct.length() != 0) {
            setTrainPercent((new Double(trainPct)).doubleValue());
        } else {
            setTrainPercent(66);
        }

        String seName = Utils.getOption('W', options);
        if (seName.length() == 0) {
            throw new Exception("A SplitEvaluator must be specified with" + " the -W option.");
        }
        // Do it first without options, so if an exception is thrown during
        // the option setting, listOptions will contain options for the actual
        // SE.
        setSplitEvaluator((SplitEvaluator) Utils.forName(SplitEvaluator.class, seName, null));
        if (getSplitEvaluator() instanceof OptionHandler) {
            ((OptionHandler) getSplitEvaluator()).setOptions(Utils.partitionOptions(options));
        }
    }

    /**
     * Gets the current settings of the result producer.
     * 
     * @return an array of strings suitable for passing to setOptions
     */
    @Override
    public String[] getOptions() {

        String[] seOptions = new String[0];
        if ((m_SplitEvaluator != null) && (m_SplitEvaluator instanceof OptionHandler)) {
            seOptions = ((OptionHandler) m_SplitEvaluator).getOptions();
        }

        String[] options = new String[seOptions.length + 9];
        int current = 0;

        options[current++] = "-P";
        options[current++] = "" + getTrainPercent();

        if (getRawOutput()) {
            options[current++] = "-D";
        }

        if (!getRandomizeData()) {
            options[current++] = "-R";
        }

        options[current++] = "-O";
        options[current++] = getOutputFile().getName();

        if (getSplitEvaluator() != null) {
            options[current++] = "-W";
            options[current++] = getSplitEvaluator().getClass().getName();
        }
        options[current++] = "--";

        System.arraycopy(seOptions, 0, options, current, seOptions.length);
        current += seOptions.length;
        while (current < options.length) {
            options[current++] = "";
        }
        return options;
    }

    /**
     * Gets a text descrption of the result producer.
     * 
     * @return a text description of the result producer.
     */
    @Override
    public String toString() {

        String result = "RandomSplitResultProducer: ";
        result += getCompatibilityState();
        if (m_Instances == null) {
            result += ": <null Instances>";
        } else {
            result += ": " + Utils.backQuoteChars(m_Instances.relationName());
        }
        return result;
    }

} // RandomSplitResultProducer
